Abstract. Impacts of a range of climate change on marine organisms have been analysed in laboratory and experimental studies. The use of different taxonomic groupings, and assessment of different processes, though, makes identifying overall trends challenging, and may mask phylogenetically different responses. Bivalve molluscs are an ecologically and economically important data-rich clade, allowing for assessment of individual vulnerability and across developmental stages. We use meta-analysis of 203 unique experimental setups to examine how bivalve growth rates respond to increased water temperature, acidity, deoxygenation, changes to salinity, and combinations of these drivers. Results show that anthropogenic climate change will affect different families of bivalves disproportionally but almost unanimously negatively. Almost all drivers and their combinations have significant negative effects on growth. Combined deoxygenation, acidification, and temperature shows the largest negative effect size. Eggs/larval bivalves are more vulnerable overall than either juveniles or adults. Infaunal taxa, including Tellinidae and Veneridae, appear more resistant to warming and oxygen reduction than epifaunal or free-swimming taxa but this assessment is based on a small number of datapoints. The current focus of experimental set-ups on commercially important taxa and families within a small range of habitats creates gaps in understanding of global impacts on these economically important foundation organisms.

This chapter is devoted specifically to count data for three reasons: (i) they are common in ecological studies (e.g. clutch sizes, numbers of fledglings from a nest, numbers of seeds per pod...); (ii) they are simple to collect and are therefore often the data collected by students (e.g. numbers of beetles in a pitfall trap, number of pollinator visits to flowers...); and (iii) they pose numerous issues that linear models with their normal error structure cannot deal with. Two studies will be examined with the response variable being counts, starting with one that nearly fits the ideals of a Poisson distribution well, the other less so. Example 1 deals with fledgling numbers in relation to clutch initiation date. The data are on the northern cardinal bird, Cardinalis cardinalis, and were collected to test the hypothesis that birds that start their clutches later may suffer higher pre-fledging offspring mortality. Example 2 focuses on pollinator flower visits in Passiflora speciosa in relation to flower size.

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Several packages have been developed to allow R-users to work with phylogenetic trees, something that most biologists will need to do at some point in their careers. The most basic is the ape package, which stands for Analysis of Phylogenetics and Evolution. This chapter gives some of the basics of handling 'trees' in R and show things that can be calculated with them. Phytools, another package with extra capabilities, are also introduced in this chapter. Insects are given as examples.

R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. In addition to its statistical and graphical capabilities, R is a programming language suitable for medium-sized projects. This book presents a set of studies that collectively represent almost all the R operations that beginners, analysing their own data up to perhaps the early years of doing a PhD, need. Although the chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping and text parsing, examples have been chosen based largely on real scientific studies at the appropriate level and within each the use of more R functions is nearly always covered than are simply necessary just to get a p-value or a graph. R comes with around a thousand base functions which are automatically installed when R is downloaded. This book covers the use of those of most relevance to biological data analysis, modelling and graphics. Throughout each chapter, the functions introduced and used in that chapter are summarized in Tool Boxes. The book also shows the user how to adapt and write their own code and functions. A selection of base functions relevant to graphics that are not necessarily covered in the main text are described in Appendix 1, and additional housekeeping functions in Appendix 2.